NeuroEngineering Research Lab

Computational Neuroscience

Computational neuroscience refers to the development of mathematical models and computational analyses of the neural systems.

Computational Neuroscience complements experimental neuroscience, by helping to integrate, and provide a deeper analysis of, different experimental results. For example, it is through mathematical modeling that we can better understand how learning takes place in different parts of the brain.

Research Goals

Our research goals are to develop this understanding using a mathematical framework.

Our current research projects include:

modelling and analysing the dynamic behaviour of the brain

modelling and analysing learning in the brain

modelling and analysing particular circuits within the brain.

Projects

The primary aim of this project is to advance our understanding of how the brain processes auditory information and how it can make sense of acoustic signals that are often mixed with other sounds in frequency and time, depending on the current behavioural or perceptual needs. In particular, we aim to investigate the processing strategies used in the auditory cortex to enhance the perception of relevant sounds in the presence of background noise and distractors. We will develop neuronal network models that will be used to elucidate the mechanisms by which attention and plasticity modify neuronal responses in a task-dependent fashion. The models will be constrained by electrophysiological and behavioural data recorded some of the world’s top experimentalists. This understanding is likely to be relevant for other sensory modalities.

A better understanding of the impact of top-down processes on perception through a computational approach will be a significant step forward in neuroscience. The outcomes of this research will yield new insights into information processing in the brain, which is of interest to neuroscience research in general and to those working on brain-inspired computation. In addition, the procedures used will offer valuable results concerning the development, optimisation, simulation and analysis of large-scale spiking neural network models.

Hierarchical visual processing

Researchers: David Grayden, Tania Kameneva, Anthony Burkitt

While the eye is a complex structure, it is just the start of an even more complex series of processing stages for visual information in the brain. People might be surprised to hear that around 40% of the human brain is involved in one way or another with visual processing; humans are very visual animals. Once signals leave the eye they enter the brain via the thalamus, which organizes the inputs from the two eyes. Once this has occurred the signals are sent to the primary visual cortex at the rear of the brain. We usually refer to this brain area as visual area one, or V1. The processing that occurs in V1 is complex and it has taken 50 years to uncover those basic properties, starting with the Nobel Prize winning work of Hubel and Wiesel at Harvard Medical School and continued in no small part by the critical contributions made by Australian scientists.

The areas of the brain that process visual information are divided into distinct compartments, each with its own map of visual space. V1 is the first cortical area and around 30 more visual areas have been identified. In some cases information is passed serially from one area to the next, e.g. V1 to V2. However, many of the connections break these strict hierarchical rules and non-hierarchical interconnections between visual brain areas are the norm rather than the exception. All this interconnectivity offers the brain an amazing richness of processing capacity, but also makes it very challenging to establish how the brain does that processing.

In this project, we use the methods of computational neuroscience and electrophysiology to investigate the pathways within V1 and V2 and other areas to understand how information is transformed between these steps. Our findings have shown that each processing step leads to a more complex signal that is informed by the input from the outside world and the influence of higher level processing in the brain. Ultimately, what we see is determined not just by what is in the visual scene but also by what we want to find in the visual world. These questions are of a basic scientific nature but will inform future medical discoveries by uncovering some of the mysteries associated with the visual system.

Modelling and simulation of interconnected neuronal populations

The spatiotemporal coordination of neural activity is constrained by the brain’s anatomy, namely, the network of axonal fibre pathways that enable communication between distant brain regions. This project aims to simulate hundreds of distinct neuronal populations that are interconnected via a biologically realistic network of axonal fibre pathways. The neuroanatomical network will be mapped using diffusion-MRI data acquired in living humans to yield a large-scale “wiring diagram” for the whole brain, telling us which neuronal populations are to be interconnected with each other in the network model. Neuronal population dynamics will be modelled and simulated using the Morris-Lecar neural mass model. The student will use the model to systematically disrupt groups of connections, as is consistent with brain disease, and investigate the consequence of these connectivity disruptions on the spatiotemporal coordination of neural activity and the brain’s functional modules. The student will then optimise and rewire the brain’s actual neuroanatomical network to improve its resilience and robustness to attack. This project will involve numerical solution of large systems of delay differential equations and is suited to a student with a background in numerical computation. VLSCI computational resources may be utilised for this purpose.

Modelling of neural plasticity for enhanced performance of brain-machine interfaces

Researchers: Anthony Burkitt, Catherine Davey, David Grayden

Plasticity of the brain is one of the great scientific challenges of neuroscience. The aim of this project is to model the synaptic changes that occur with reward-modulated spike-timing-dependent plasticity and apply the model to developing plasticity targeted brain-machine interfaces. The significance of this approach is that such plasticity targeted techniques provide the prospect of taking advantage of the underlying neural plasticity to optimise the form of the neural recording and electrical stimulation. The outcomes will be to greatly improve the performance of brain-machine interface in terms of measures such as the number and sensitivity of channels, as well as robustness and reliability.

Modelling the human nervous system with human pluripotent stem cells.

Researchers: Mirella Dottori

The human nervous system is one of the most complex structures evolved to date. In order to understand how it functions, and dysfunctions in a diseased state, it is fundamental to decipher how it develops to generate various neuronal populations that form this elaborate network. Human stem cells provide a valuable source to study such processes. Our aim is to use human stem cells to study how early progenitor cell types that structure the nervous system are generated and how their neuronal derivatives form connectivity and functional synapses. The outcome of these studies is that we will establish a cellular model of human neurogenesis that can be utilized to study developmental disease processes.